ck:loop

Automate iterative code optimization by running specified iterations against mechanical metrics, leveraging Git history to learn and verify changes. It systematically experiments and commits only verified improvements to prevent regressions in your codebase.

Automatically increase test coverage by specifying a target metric and verification command. The optimization loop runs iterations, checks the metric, and uses Git history to roll back failed attempts, committing only changes that demonstrably improve coverage.

Git history tracking guards against regressions by providing rollback capabilities. If an optimization attempt fails to improve the specified metric, the change is rolled back, ensuring only verified improvements are committed to the repository.

Yes, you can use specific shell commands to verify code metrics. The optimization loop requires a verification command to check metrics like bundle size, ESLint errors, or test coverage, ensuring changes meet your specific criteria before committing.

Yes, a Git repository is required to run automated optimization loops. Git integration is essential for tracking changes, learning from history, and providing rollback capabilities to guard against regressions during the iterative process.